Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Computing shortest paths is a straightforward task when the network of interest is fully known, and there are a plethora of computational algorithms for this purpose. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions and the Internet at the autonomous system level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks, where numbers of missing links exceed those of observable links. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security.

Disruptions to transportation networks are inevitable. When road networks are not resilient, or in other words, do not recover rapidly from disruptions, unpredictable events can cause significant delays that may be disproportionately greater than the extent of the disruption. Enhancing transportation system resilience can help mitigate the consequences of disruptions; however, required investments are difficult to justify given the low probability of such events. This paper calculates economic implications of unmitigated random disruptions in urban road systems. We modeled delays in transportation networks and demonstrated how resilience can be integrated into macroeconomic modeling via the transportation planning model, REMI TranSight. The model was applied to 10 cities in the United States to forecast the impact of disruptions on gross domestic product (GDP). Different disruption scenarios were modeled and the magnitude of disruption was used to calculate additional delays in transportation networks, which were then integrated into the TranSight model. The results were compared to a baseline case, where economic impact was assumed to be proportional to the magnitude of disruptions. Results show that losses in GDP were far more pronounced in the case scenario as compared to the baseline. The losses tended to be higher in wealthier and more economically productive cities. The economic output tends to rebound one to two years after a disruptive event. We conclude that different topology in transportation networks in different cities requires explicit consideration and quantification of resilience to support investment decisions designed to improve transportation networks in cities.

Many cities are adopting increasingly advanced Intelligent Transportation Systems (ITS). These systems combine connectivity, coordination, adaptivity, and automated response for transportation policy optimization, thus increasing “smartness” and efficiency. However, the control and sensing systems of implemented ITS can open new vulnerabilities, especially to cyber-attacks. Currently vulnerability is managed within the framework of traditional risk assessment that assesses potential failures of the system in response to specified threats. Emerging technologies by their nature have threats that are not fully known, therefore, resilience, defined as the system's ability to recover and adapt to both known and unknown threats, is an emerging area that holds promise for assessing threats to ITS. To illustrate the applicability of resilience to ITS, we conducted a study of network efficiency and resilience in response to random and targeted disruptions of ITS systems in 10 urban areas. Disruptions were generated to affect either intersections or roadways controlled by ITS under different threat scenarios. Modeled attacks, under worst case scenarios, disrupted 20% of intersections causing on average 14.6% more additional delays than the same severity attacks on roadways. Additionally, locking traffic signal states was found to cause more disruption than fully disabling signals. Thus, as cities adopt ITS and other smart systems resulting in potentially unknown vulnerabilities, it is important to consider resilience of transportation infrastructure affected by potential cyber-attacks.